A new recognition method of vehicle license plate based on Genetic Neural Network

• Main Authors: Guangmin Sun, Canhui Zhang, Weiwei Zou, Guangyu Yu
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Character recognition; Feature extraction; GABP; Genetic algorithms; global optimal solution; Licenses; Neural networks; Signal processing algorithms; Statistics; Sun; Vehicles
• ISSN: 2156-2318; 2158-2297
• DOI: 10.1109/ICIEA.2010.5515189

A new recognition method of vehicle license plates based on neural network is presented in this paper. For the Back Propagation (BP) neural network often trap into the local minimum in the training process, a Genetic Neural Network (GNN), GABP was constructed by combining the Genetic Algorithm(GA)with BP neural network. The training of the GABP neural network was finished in two steps. The GA was firstly used to make a thorough searching in the global space for the weights and thresholds of the neural network, which can ensure they fall into the neighborhood of global optimal solution. Then, in order to improve the convergence precision, the gradient method was used to finely train the network and find the global optimum or second-best solution with good performance. On the other side, feature extraction is also important for improving the recognition rate of the network. So both the structure features and the statistic features are used in this paper, which include mesh feature, direction line element feature and Zernike moments feature. Experimental results show that the proposed method can save the time of training network and achieve a highly recognition rate.